mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 20:28:20 +03:00
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
from __future__ import unicode_literals
|
|
import random
|
|
import numpy.random
|
|
|
|
from ..pipeline import TextCategorizer
|
|
from ..lang.en import English
|
|
from ..vocab import Vocab
|
|
from ..tokens import Doc
|
|
from ..gold import GoldParse
|
|
|
|
|
|
def test_textcat_learns_multilabel():
|
|
random.seed(0)
|
|
numpy.random.seed(0)
|
|
docs = []
|
|
nlp = English()
|
|
vocab = nlp.vocab
|
|
letters = ['a', 'b', 'c']
|
|
for w1 in letters:
|
|
for w2 in letters:
|
|
cats = {letter: float(w2==letter) for letter in letters}
|
|
docs.append((Doc(vocab, words=['d']*3 + [w1, w2] + ['d']*3), cats))
|
|
random.shuffle(docs)
|
|
model = TextCategorizer(vocab, width=8)
|
|
for letter in letters:
|
|
model.add_label(letter)
|
|
optimizer = model.begin_training()
|
|
for i in range(30):
|
|
losses = {}
|
|
Ys = [GoldParse(doc, cats=cats) for doc, cats in docs]
|
|
Xs = [doc for doc, cats in docs]
|
|
model.update(Xs, Ys, sgd=optimizer, losses=losses)
|
|
random.shuffle(docs)
|
|
for w1 in letters:
|
|
for w2 in letters:
|
|
doc = Doc(vocab, words=['d']*3 + [w1, w2] + ['d']*3)
|
|
truth = {letter: w2==letter for letter in letters}
|
|
model(doc)
|
|
for cat, score in doc.cats.items():
|
|
if not truth[cat]:
|
|
assert score < 0.5
|
|
else:
|
|
assert score > 0.5
|
|
|